• Part 0: A little bit of set up!
• Part 1: reading in manually (point and click)
• Part 2: reading in directly, reading XLSX file (Excel file), other data inputs
• Part 3: working directories, relative vs. absolute paths

We will cover Output a bit later!

Let’s make an R Project so we can stay organized in the next steps.

Click the new R Project button at the top left of RStudio:

In the New Project Wizard, click “New Directory”:

Click “New Project”:

Type in a name for your new folder.

Store it somewhere easy to find, such as your Desktop:

You now have a new R Project folder on your Desktop!

Make sure you add any scripts or data files to this folder as we go through today’s lesson. This will make sure R is able to “find” your files.

• ‘Reading in’ data is the first step of any real project/analysis
• R can read almost any file format, especially via add-on packages
• We are going to focus on simple delimited files first
  • comma separated (e.g. ‘.csv’)
  • tab delimited (e.g. ‘.txt’)
  • Microsoft Excel (e.g. ‘.xlsx’)

• We have added functionality to load some datasets directly in the jhur package

Youth Tobacco Survey (YTS) dataset:

“The YTS was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors’ ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages.”

• Check out the data at: https://catalog.data.gov/dataset/youth-tobacco-survey-yts-data

Dataset is located at http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv

• Download data by clicking the above link

  • Safari - if a file loads in your browser, choose File –> Save As, select, Format “Page Source” and save

File Import Dataset From Text (readr) paste the url (http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv) click “Update” and “Import”

You see a preview of the data on the top left pane.

You see a new object called Youth_Tobacco_Survey_YTS_Data in your environment pane (top right). The table button opens the data for you to view.

R ran some code in the console (bottom left).

Pros: easy!! Cons: obscures some of what’s happening, others will have difficulty running your code

Review the process: https://youtu.be/LEkNfJgpunQ

File Import Dataset From Text (readr) paste the url (http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv) click “Update” and “Import”

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💻 Data I/O Lab

# load library `readr` that contains function `read_csv`
library(readr)
dat <- read_csv(file = "http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv")

# `head` displays first few rows of a data frame. `tail()` works the same way.
head(dat, n = 5)

# A tibble: 5 × 31
   YEAR LocationAbbr LocationDesc TopicType     TopicDesc MeasureDesc DataSource
  <dbl> <chr>        <chr>        <chr>         <chr>     <chr>       <chr>     
1  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
2  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
3  2015 AZ           Arizona      Tobacco Use … Cessatio… Percent of… YTS       
4  2015 AZ           Arizona      Tobacco Use … Cessatio… Quit Attem… YTS       
5  2015 AZ           Arizona      Tobacco Use … Cessatio… Quit Attem… YTS       
# … with 24 more variables: Response <chr>, Data_Value_Unit <chr>,
#   Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
#   Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>, Sample_Size <dbl>,
#   Gender <chr>, Race <chr>, Age <chr>, Education <chr>, GeoLocation <chr>,
#   TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
#   StratificationID2 <chr>, StratificationID3 <chr>, …

dat <- read_csv(file = "http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv")
# EQUIVALENT TO
dat <- read_csv("http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv")

So what is going on “behind the scenes”?

read_csv() parses a “flat” text file (.csv) and turns it into a tibble – a rectangular data frame, where data are split into rows and columns

• First, a flat file is parsed into a rectangular matrix of strings

• Second, the type of each column is determined (heuristic-based guess)

read_csv() needs the path to your file. It will return a tibble

read_csv(file, col_names = TRUE, col_types = NULL,
  locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
  skip = 0, n_max = Inf, guess_max = min(1000, n_max),
  progress = show_progress(), skip_empty_rows = TRUE
)

• file is the path to your file, in quotes
• can be path in your local computer – absolute file path or relative file path
• can be path to a file on a website

## Examples

dat <- read_csv(file = "/Users/avahoffman/Downloads/Youth_Tobacco_Survey_YTS_Data.csv")

dat <- read_csv(file = "Youth_Tobacco_Survey_YTS_Data.csv")

dat <- read_csv(file = "www.someurl.com/table1.csv")

Great, but what is my “path”?

Luckily, we already set up an R Project!

If we add the Youth_Tobacco_Survey_YTS_Data.csv file to the intro_to_r folder, we can use the relative path:

dat <- read_csv(file = "Youth_Tobacco_Survey_YTS_Data.csv")

read_csv() is a special case of read_delim() – a general function to read a delimited file into a data frame

read_delim() needs path to your file and file’s delimiter, will return a tibble

read_delim(file, delim, quote = "\"", escape_backslash = FALSE, 
  escape_double = TRUE, col_names = TRUE,  col_types = NULL, 
  locale = default_locale(),na = c("", "NA"),  quoted_na = TRUE, 
  comment = "", trim_ws = FALSE, skip = 0, 
  n_max = Inf,  guess_max = min(1000, n_max), 
  progress = show_progress(), skip_empty_rows = TRUE
)

• file is the path to your file, in quotes
• delim is what separates the fields within a record

## Examples
dat <- read_delim(file = "Youth_Tobacco_Survey_YTS_Data.csv", delim = ",")

dat <- read_delim(file = "www.someurl.com/table1.txt", delim = "\t")

Move the data to the data folder and change the relative path:

dat <- read_csv(file = "data/Youth_Tobacco_Survey_YTS_Data.csv")

The data is now successfully read into your R environment. You can confirm this by checking the “Environment” pane (top right). Column specification of first few columns is printed to the console.

1. Working directory problems: trying to read files that R “can’t find”
   • Path misspecification
   • more on this shortly!
2. Typos (R is case sensitive, x and X are different)
   • RStudio helps with “tab completion”
3. Open ended quotes, parentheses, and brackets
   
4. Different versions of software
5. Deleting part of the code chunk

• the View() function shows your data in a new tab, in spreadsheet format
• be careful if your data is big!

View(dat)

The str() function shows you the structure of the data (different variables and their classes - more on this later).

str(dat)

spec_tbl_df [9,794 × 31] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ YEAR                      : num [1:9794] 2015 2015 2015 2015 2015 ...
 $ LocationAbbr              : chr [1:9794] "AZ" "AZ" "AZ" "AZ" ...
 $ LocationDesc              : chr [1:9794] "Arizona" "Arizona" "Arizona" "Arizona" ...
 $ TopicType                 : chr [1:9794] "Tobacco Use – Survey Data" "Tobacco Use – Survey Data" "Tobacco Use – Survey Data" "Tobacco Use – Survey Data" ...
 $ TopicDesc                 : chr [1:9794] "Cessation (Youth)" "Cessation (Youth)" "Cessation (Youth)" "Cessation (Youth)" ...
 $ MeasureDesc               : chr [1:9794] "Percent of Current Smokers Who Want to Quit" "Percent of Current Smokers Who Want to Quit" "Percent of Current Smokers Who Want to Quit" "Quit Attempt in Past Year Among Current Cigarette Smokers" ...
 $ DataSource                : chr [1:9794] "YTS" "YTS" "YTS" "YTS" ...
 $ Response                  : chr [1:9794] NA NA NA NA ...
 $ Data_Value_Unit           : chr [1:9794] "%" "%" "%" "%" ...
 $ Data_Value_Type           : chr [1:9794] "Percentage" "Percentage" "Percentage" "Percentage" ...
 $ Data_Value                : num [1:9794] NA NA NA NA NA NA 3.2 3.2 3.1 12.5 ...
 $ Data_Value_Footnote_Symbol: chr [1:9794] "*" "*" "*" "*" ...
 $ Data_Value_Footnote       : chr [1:9794] "Data in these cells have been suppressed because of a small sample size" "Data in these cells have been suppressed because of a small sample size" "Data in these cells have been suppressed because of a small sample size" "Data in these cells have been suppressed because of a small sample size" ...
 $ Data_Value_Std_Err        : num [1:9794] NA NA NA NA NA NA 1.5 1.5 1.6 2.7 ...
 $ Low_Confidence_Limit      : num [1:9794] NA NA NA NA NA NA 0.3 0.3 0.1 7.2 ...
 $ High_Confidence_Limit     : num [1:9794] NA NA NA NA NA NA 6.1 6.2 6.1 17.9 ...
 $ Sample_Size               : num [1:9794] NA NA NA NA NA ...
 $ Gender                    : chr [1:9794] "Overall" "Male" "Female" "Overall" ...
 $ Race                      : chr [1:9794] "All Races" "All Races" "All Races" "All Races" ...
 $ Age                       : chr [1:9794] "All Ages" "All Ages" "All Ages" "All Ages" ...
 $ Education                 : chr [1:9794] "Middle School" "Middle School" "Middle School" "Middle School" ...
 $ GeoLocation               : chr [1:9794] "(34.865970280000454, -111.76381127699972)" "(34.865970280000454, -111.76381127699972)" "(34.865970280000454, -111.76381127699972)" "(34.865970280000454, -111.76381127699972)" ...
 $ TopicTypeId               : chr [1:9794] "BEH" "BEH" "BEH" "BEH" ...
 $ TopicId                   : chr [1:9794] "105BEH" "105BEH" "105BEH" "105BEH" ...
 $ MeasureId                 : chr [1:9794] "170CES" "170CES" "170CES" "169QUA" ...
 $ StratificationID1         : chr [1:9794] "1GEN" "2GEN" "3GEN" "1GEN" ...
 $ StratificationID2         : chr [1:9794] "8AGE" "8AGE" "8AGE" "8AGE" ...
 $ StratificationID3         : chr [1:9794] "6RAC" "6RAC" "6RAC" "6RAC" ...
 $ StratificationID4         : chr [1:9794] "1EDU" "1EDU" "1EDU" "1EDU" ...
 $ SubMeasureID              : chr [1:9794] "YTS01" "YTS02" "YTS03" "YTS04" ...
 $ DisplayOrder              : num [1:9794] 1 2 3 4 5 6 7 7 7 8 ...
 - attr(*, "spec")=
  .. cols(
  ..   YEAR = col_double(),
  ..   LocationAbbr = col_character(),
  ..   LocationDesc = col_character(),
  ..   TopicType = col_character(),
  ..   TopicDesc = col_character(),
  ..   MeasureDesc = col_character(),
  ..   DataSource = col_character(),
  ..   Response = col_character(),
  ..   Data_Value_Unit = col_character(),
  ..   Data_Value_Type = col_character(),
  ..   Data_Value = col_double(),
  ..   Data_Value_Footnote_Symbol = col_character(),
  ..   Data_Value_Footnote = col_character(),
  ..   Data_Value_Std_Err = col_double(),
  ..   Low_Confidence_Limit = col_double(),
  ..   High_Confidence_Limit = col_double(),
  ..   Sample_Size = col_double(),
  ..   Gender = col_character(),
  ..   Race = col_character(),
  ..   Age = col_character(),
  ..   Education = col_character(),
  ..   GeoLocation = col_character(),
  ..   TopicTypeId = col_character(),
  ..   TopicId = col_character(),
  ..   MeasureId = col_character(),
  ..   StratificationID1 = col_character(),
  ..   StratificationID2 = col_character(),
  ..   StratificationID3 = col_character(),
  ..   StratificationID4 = col_character(),
  ..   SubMeasureID = col_character(),
  ..   DisplayOrder = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

For any function, you can write ?FUNCTION_NAME, or help("FUNCTION_NAME") to look at the help file:

?read_delim
help("read_delim")

There are also data importing functions provided in base R (rather than the readr package), like read.delim() and read.csv().

These functions have slightly different syntax for reading in data (e.g. header argument).

However, while many online resources use the base R tools, the latest version of RStudio switched to use these new readr data import tools, so we will use them in the class for slides. They are also up to two times faster for reading in large datasets, and have a progress bar which is nice.

• haven package has functions to read SAS, SPSS, Stata formats

library(haven)

# SAS
read_sas(file = "mtcars.sas7bdat")

# SPSS
read_sav(file = "mtcars.sav")

# Stata
read_dta(file = "mtcars.dta")

• Modern, improved tools from readr R package: read_delim(), read_csv()
  • needs a file path to be provided
  • parses the file into rows/columns, determines column type
  • returns a tibble (data frame)
• Some functions to look at a data frame:
  • head() shows first few rows
  • tail() shows the last few rows
  • View() shows the data as a spreadsheet
  • str() tells you about column types

• From readr package:
  • read_delim(): general delimited files
  • read_csv(): comma separated (CSV) files
  • read_tsv(): tab separated files
  • others
• For reading Excel files, you can do one of:
  • use read_excel() function from readxl package
  • use other packages: xlsx, openxlsx

🏠 Class Website

💻 Data I/O Lab

Working directory is a directory that R assumes “you are working in”. It’s where R looks for files.

“Setting working directory” means specifying the path to the directory.

# get the working directory
getwd()

# set the working directory
setwd("/Users/avahoffman/Desktop")

R uses working directory as a starting place when searching for files.

R uses working directory as a starting place when searching for files:

• if you use read_csv("Bike_Lanes_Long.csv"), R assumes that the file is in the working directory

• if you use read_csv("data/Bike_Lanes_Long.csv"), R assumes that data directory is in the working directory

• if you use an absolute path, e.g. read_csv("/Users/avahoffman/data/Bike_Lanes_Long.csv"), the working directory information is not used

Setting up an R Project can avoid headaches by telling R that the working directory is wherever the .Rproj file is.

• R Projects are a good way to keep your files organized and reduce headaches
• Use read_csv() and read_delim() from the readr package to read in your data
• Don’t forget to use <- to assign your data to an object!
• Use str() to understand objects
• Use head() and tail() to preview the first and last lines of the data

🏠 Class Website

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Image by Gerd Altmann from Pixabay